This application is related to applicant's co-pending application Ser. No. 08/323,238 filed Oct. 13, 1994, entitled Device For The Autonomous Generation Of Useful Information, in which the "creativity machine" paradigm was introduced. The creativity machine paradigm involves progressively purturbing a first neural network having a predetermined knowledge domain such that the perturbed network continuously outputs a stream of concepts, and monitoring the outputs or stream of concepts with a second neural network which is trained to identify only useful concepts. The perturbations may be achieved by different means, including the introduction of noise to the network, or degradation of the network. Importantly, the present application provides an excellent system for constructing such creativity machines, and further builds upon the creativity machine invention to achieve self training neural networks.
The current explosion of information has made it necessary to develop new techniques for handling and analyzing such information. In this regard, it would be helpful to be able to effectively discover regularities and trends within data and to be able to effectively sort and/or organize data. Currently, various algorithmic techniques and systems may be utilized to analyze data, however, such techniques and systems generally fail to display the creativity needed to enable them to organize the data and exhaust sets of data of all potential discoveries. The use of neural networks for such tasks would be advantageous.
Further, the advantages of new artificial neural networks (ANNs) are ever increasing. Currently, such artificial neural networks are often trained and implemented algorithmically. These techniques require the skills of a neural network specialist who may spend many hours developing the training and/or implementation software for such algorithms. Further, when using algorithms to train artificial neural networks, once new training data is obtained, the new training data must be manually appended to the preexisting set of training data and network training must be reinitiated, requiring additional man hours. Disadvantageously, if the newly acquired training data does not fit the pattern of preexisting training data, the generalization capacity of the network may be lowered.
An additional drawback to traditional algorithm implemented training and operation of artificial neural networks is that within such schemes, individual activation levels are only momentarily visible and accessible, as when the governing algorithm evaluates the sigmoidal excitation of any given node or neuron. Except for this fleeting appearance during program execution, a neuron's excitation, or activation level, is quickly obscured by redistribution among downstream processing elements.
Accordingly, it is desirable and advantageous to provide a simpler method of training, implementing, and simulating artificial neural networks. It is further desirable to provide artificial neural networks which can be easily cascaded together to facilitate the construction of more complex artificial neural network systems. It also is desirable and advantageous to provide neural networks which can be configured to perform a variety of tasks, including self training artificial neural networks, as well as networks capable of analyzing, sorting, and organizing data.
A principal object of the present invention is to provide a user friendly system of implementing or simulating neural networks in which movement of such networks and cascading of such networks is facilitated.
Another object of the present invention is to provide self training artificial neural networks.
A further object of the present invention is to provide artificial neural networks capable of analyzing data within a data space.
Yet another object of the present invention is to provide artificial neural networks which are mobile within a data space.
Still another object of the present invention is to provide artificial neural networks which can be easily duplicated within a data space and which can be easily interconnected to facilitate the construction of more complex artificial neural network systems.